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Emergent behaviours based on episodic encoding and familiarity driven retrieval

  • Emilia I. Barakova
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3192)

Abstract

In analogy to animal research, where behavioural and internal neu-ral dynamics are simultaneously analysed, this paper suggests a method for emergent behaviours arising in interaction with the underlying neural mech-anism. This way an attempt to go beyond the indeterministic nature of the emergent behaviours of robots is made. The neural dynamics is represented as an interaction of memories of experienced episodes, the current environ-mental input and the feedback of previous motor actions. The emergent prop-erties can be observed in a two staged process: exploratory (latent) learning and goal oriented learning. Correspondingly, the learning is dominated to a different extent by two factors: novelty and reward. While the reward learn-ing is used to show the relevance of the method, the novelty/familiarity is a basis for forming the emergent properties. The method is strongly inspired by the state of the art understanding of the hippocampal functioning and espe-cially its role in novelty detection and episodic memory formation in relation to spatial context.

Keywords

Episodic Memory Place Cell Emergent Behaviour Hebbian Learning Novelty Detection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Emilia I. Barakova
    • 1
  1. 1.RIKEN BSISaitamaJapan

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